Molecular Design TRIZ Cases

A computer-based method maps spatial relationships between query ligand substructures and target macromolecules to generate 3-D structural models.

Computational method predicts ligand modifications by mapping non-bonding atom contacts to protein binding sites.

Computational protocol identifies cryptic binding pockets in tau protein fragments to stabilize specific conformations and prevent aggregation.

A binding assay method utilizes saturated receptor measurement in n-curve analysis to determine Kd and Rt values without completing the full concentration range.

Computational method partitions lead compounds into core and non-core regions to identify alternative cores.

A method segments organic molecules into standardized fragments to automatically generate all possible stereoisomer combinations.

Target neural network processes molecular representations via distributed parallel computing to accelerate retrosynthesis route generation.

A computational drug discovery system performs pose and free energy calculations to determine ligand-receptor interactions.

Multi-level graph prompt learning integrates entity and interaction graphs to resolve the accuracy-complexity trade-off in drug reaction prediction.

Continuous latent space optimization via variational autoencoders enables gradient-based scaffold decoration for specific protein targets.

A reinforcement learning model infers molecular structures using a tree representation with site information to maintain connection data.

Transition state analogs mimic DNMT1 geometry to inhibit enzyme activity, avoiding mutagenic DNA incorporation found in current cancer therapies.

Computational docking predicts drug efficacy against individual mutations, reducing development time and side effects.

A computational model estimates receptor sensitivity using solubility parameters to output optimized molecule structures.

Automated iterative widening search refines chemical compound structures using tolerance functions to identify viable molecular alternatives.

The Movable Type method estimates ligand poses and binding free energies using statistical mechanics to combine pairwise energy databases.

SILCS FragMaps map hydrophobic and aromatic affinity patterns via explicit solvent simulations, resolving protein conformational heterogeneity.

A method probes protein binding sites by calculating molecular dynamics trajectories and integrating tensors with experimental data.

An expert-in-the-loop system filters non-viable polymer candidates using synthetic viability criteria, reducing manual review time.

An optical biomodule detects disease-specific biomarkers using enhanced fluorescence emission from three-dimensional protruded structures in fluidic containers.

Conditioning virtual high throughput screening models with negative pose data enables accurate interaction characterization between test compounds and target polymers.

Double-integration orthogonal space tempering resolves accuracy and efficiency trade-offs in binding affinity predictions.

Scheduled sampling aligns pretraining with generation to reduce exposure bias while a self-defined reward controls specific compound characteristics.

An in-silico system generates drug-target-phenotype networks to predict cellular and clinical outcomes.

Automated screening clusters pharmaceutical targets by chemical properties, reducing search space and accelerating drug discovery.

A deep neural network identifies expression regions in chemical descriptors to generate new molecular structures with specific property values.

A digital passport links decentral identifiers to industrial formulator ingredients.

A neural network model trained on pooled data using secret sharing protocols predicts drug-target interactions while reducing computation overhead.

A memory network generates molecules by sequentially processing token strings representing molecular scaffolds and sampling candidate tokens at open positions.

A virtual screening system generates novel drug compounds using AI models trained on crystal complex data.

A machine learning model predicts small molecule lipophilicity using liquid chromatography retention times.

Artificial neural network computes herbal ingredient relevance indices to systematically identify effective Traditional Chinese Medicine treatments.

A virtual drug screening system extracts bioassay data to calculate enrichment scores for compound selection.

A multi-modal deep learning model generates target-specific molecules using graph attention and SMILES variational autoencoders.

A differential binding score method identifies preferred ligand binding sites on intrinsically disordered protein ensembles.

Decision boundary rules encode expert knowledge to filter generated candidates, reducing human validation time while improving discovery efficiency.

A trained neural network predicts sequential bond additions to generate valid small molecule compounds.